wip duckdb
This commit is contained in:
Dobromir Popov
@@ -352,6 +352,9 @@ class DataProvider:
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# Start COB WebSocket integration
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self.start_cob_websocket_integration()
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# Start periodic DuckDB storage of live data
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self._start_periodic_duckdb_storage()
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# ===================================================================
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# UNIFIED STORAGE SYSTEM METHODS
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# ===================================================================
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@@ -700,7 +703,7 @@ class DataProvider:
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self.catch_up_completed = True
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def _update_cached_data(self, symbol: str, timeframe: str):
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"""Update cached data by fetching last 2 candles with thread-safe locking"""
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"""Update cached data by fetching last 2 candles and storing to DuckDB"""
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try:
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# Fetch last 2 candles (outside lock - network I/O)
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df = self._fetch_from_binance(symbol, timeframe, 2)
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@@ -712,6 +715,14 @@ class DataProvider:
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# Ensure proper datetime index
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df = self._ensure_datetime_index(df)
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# Store to DuckDB immediately (live data persistence)
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if self.duckdb_storage:
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try:
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self.duckdb_storage.store_ohlcv_data(symbol, timeframe, df)
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logger.debug(f"Stored live data to DuckDB: {symbol} {timeframe} ({len(df)} candles)")
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except Exception as e:
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logger.warning(f"Could not store live data to DuckDB: {e}")
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# Update cached data with lock
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with self.data_lock:
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existing_df = self.cached_data[symbol][timeframe]
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@@ -722,7 +733,7 @@ class DataProvider:
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combined_df = combined_df[~combined_df.index.duplicated(keep='last')]
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combined_df = combined_df.sort_index()
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# Keep only last 1500 candles
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# Keep only last 1500 candles in memory
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self.cached_data[symbol][timeframe] = combined_df.tail(1500)
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else:
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self.cached_data[symbol][timeframe] = df
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@@ -2037,10 +2048,22 @@ class DataProvider:
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# === WILLIAMS MARKET STRUCTURE PIVOT SYSTEM ===
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def _collect_monthly_1m_data(self, symbol: str) -> Optional[pd.DataFrame]:
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"""Collect 30 days of 1m candles with smart gap-filling cache system"""
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"""Collect 30 days of 1m candles with DuckDB cache system"""
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try:
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# Check for cached data and determine what we need to fetch
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cached_data = self._load_monthly_data_from_cache(symbol)
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# Check for cached data in DuckDB
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cached_data = None
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if self.duckdb_storage:
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try:
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end_time_check = datetime.utcnow()
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start_time_check = end_time_check - timedelta(days=30)
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cached_data = self.duckdb_storage.get_ohlcv_data(
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symbol=symbol,
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timeframe='1m',
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start_time=start_time_check,
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end_time=end_time_check
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)
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except Exception as e:
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logger.debug(f"No cached monthly data in DuckDB: {e}")
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import pytz
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utc = pytz.UTC
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@@ -2097,8 +2120,12 @@ class DataProvider:
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logger.info(f"Final dataset: {len(monthly_df)} 1m candles for {symbol}")
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# Update cache
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self._save_monthly_data_to_cache(symbol, monthly_df)
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# Update DuckDB cache
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if self.duckdb_storage:
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try:
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self.duckdb_storage.store_ohlcv_data(symbol, '1m', monthly_df)
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except Exception as e:
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logger.warning(f"Could not cache monthly data in DuckDB: {e}")
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return monthly_df
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else:
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@@ -2473,8 +2500,37 @@ class DataProvider:
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# Store bounds
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self.pivot_bounds[symbol] = bounds
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# Save to cache
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self._save_pivot_bounds_to_cache(symbol, bounds)
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# Save to DuckDB as JSON
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if self.duckdb_storage:
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try:
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import pickle
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bounds_json = {
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'symbol': symbol,
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'price_max': bounds.price_max,
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'price_min': bounds.price_min,
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'volume_max': bounds.volume_max,
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'volume_min': bounds.volume_min,
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'pivot_support_levels': bounds.pivot_support_levels,
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'pivot_resistance_levels': bounds.pivot_resistance_levels,
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'created_timestamp': bounds.created_timestamp.isoformat(),
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'data_period_start': bounds.data_period_start.isoformat(),
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'data_period_end': bounds.data_period_end.isoformat(),
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'total_candles_analyzed': bounds.total_candles_analyzed
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}
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# Store in DuckDB metadata table
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self.duckdb_storage.conn.execute("""
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CREATE TABLE IF NOT EXISTS pivot_bounds_cache (
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symbol VARCHAR PRIMARY KEY,
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bounds_data JSON,
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updated_at BIGINT
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)
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""")
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self.duckdb_storage.conn.execute("""
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INSERT OR REPLACE INTO pivot_bounds_cache (symbol, bounds_data, updated_at)
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VALUES (?, ?, ?)
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""", (symbol, json.dumps(bounds_json), int(datetime.now().timestamp() * 1000)))
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except Exception as e:
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logger.warning(f"Could not save pivot bounds to DuckDB: {e}")
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logger.info(f"Successfully refreshed pivot bounds for {symbol}")
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@@ -2530,136 +2586,83 @@ class DataProvider:
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return symbol # Return first symbol for now - can be improved
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return None
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# === CACHE MANAGEMENT ===
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# === DUCKDB CACHE MANAGEMENT ===
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def _auto_fix_corrupted_cache(self):
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"""Automatically fix corrupted cache files on startup"""
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def _start_periodic_duckdb_storage(self):
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"""Start periodic task to store live data to DuckDB"""
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def storage_worker():
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"""Worker thread that periodically stores cached data to DuckDB"""
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import time
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logger.info("Started periodic DuckDB storage worker (every 60 seconds)")
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while True:
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try:
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from utils.cache_manager import get_cache_manager
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cache_manager = get_cache_manager()
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time.sleep(60) # Store every minute
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# Quick health check
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health_summary = cache_manager.get_cache_summary()
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if not self.duckdb_storage:
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continue
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if health_summary['corrupted_files'] > 0:
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logger.warning(f"Found {health_summary['corrupted_files']} corrupted cache files, cleaning up...")
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# Auto-cleanup corrupted files (no confirmation needed)
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deleted_files = cache_manager.cleanup_corrupted_files(dry_run=False)
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deleted_count = 0
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for cache_dir, files in deleted_files.items():
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for file_info in files:
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if "DELETED:" in file_info:
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deleted_count += 1
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logger.info(f"Auto-cleaned {deleted_count} corrupted cache files")
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else:
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logger.debug("Cache health check passed - no corrupted files found")
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except Exception as e:
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logger.warning(f"Cache auto-fix failed: {e}")
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# === PIVOT BOUNDS CACHING ===
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def _load_all_pivot_bounds(self):
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"""Load all cached pivot bounds on startup"""
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try:
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# Store all cached data to DuckDB
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for symbol in self.symbols:
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bounds = self._load_pivot_bounds_from_cache(symbol)
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if bounds:
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self.pivot_bounds[symbol] = bounds
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logger.info(f"Loaded cached pivot bounds for {symbol}")
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except Exception as e:
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logger.error(f"Error loading pivot bounds from cache: {e}")
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for timeframe in self.timeframes:
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with self.data_lock:
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df = self.cached_data.get(symbol, {}).get(timeframe)
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def _load_pivot_bounds_from_cache(self, symbol: str) -> Optional[PivotBounds]:
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"""Load pivot bounds from cache"""
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if df is not None and not df.empty:
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try:
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cache_file = self.pivot_cache_dir / f"{symbol.replace('/', '')}_pivot_bounds.pkl"
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if cache_file.exists():
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with open(cache_file, 'rb') as f:
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bounds = pickle.load(f)
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# Store last 100 candles to avoid duplicates
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self.duckdb_storage.store_ohlcv_data(symbol, timeframe, df.tail(100))
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logger.debug(f"Periodic storage: {symbol} {timeframe} ({len(df.tail(100))} candles)")
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except Exception as e:
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logger.warning(f"Error in periodic storage for {symbol} {timeframe}: {e}")
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# Check if bounds are still valid (not too old)
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except Exception as e:
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logger.error(f"Error in periodic DuckDB storage worker: {e}")
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# Start worker thread
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storage_thread = Thread(target=storage_worker, daemon=True, name="DuckDBStorageWorker")
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storage_thread.start()
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logger.info("Periodic DuckDB storage worker started")
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def _load_pivot_bounds_from_duckdb(self, symbol: str) -> Optional[PivotBounds]:
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"""Load pivot bounds from DuckDB"""
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if not self.duckdb_storage:
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return None
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try:
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result = self.duckdb_storage.conn.execute("""
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SELECT bounds_data FROM pivot_bounds_cache WHERE symbol = ?
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""", (symbol,)).fetchone()
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if result:
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bounds_json = json.loads(result[0])
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bounds = PivotBounds(
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symbol=bounds_json['symbol'],
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price_max=bounds_json['price_max'],
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price_min=bounds_json['price_min'],
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volume_max=bounds_json['volume_max'],
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volume_min=bounds_json['volume_min'],
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pivot_support_levels=bounds_json['pivot_support_levels'],
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pivot_resistance_levels=bounds_json['pivot_resistance_levels'],
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pivot_context={},
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created_timestamp=datetime.fromisoformat(bounds_json['created_timestamp']),
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data_period_start=datetime.fromisoformat(bounds_json['data_period_start']),
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data_period_end=datetime.fromisoformat(bounds_json['data_period_end']),
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total_candles_analyzed=bounds_json['total_candles_analyzed']
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)
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# Check if cache is recent
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age = datetime.now() - bounds.created_timestamp
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if age <= self.pivot_refresh_interval:
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if age < self.pivot_refresh_interval:
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logger.debug(f"Loaded pivot bounds from DuckDB for {symbol}")
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return bounds
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else:
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logger.info(f"Cached pivot bounds for {symbol} are too old ({age.days} days)")
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return None
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except Exception as e:
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logger.warning(f"Error loading pivot bounds from cache for {symbol}: {e}")
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return None
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def _save_pivot_bounds_to_cache(self, symbol: str, bounds: PivotBounds):
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"""Save pivot bounds to cache"""
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try:
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cache_file = self.pivot_cache_dir / f"{symbol.replace('/', '')}_pivot_bounds.pkl"
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with open(cache_file, 'wb') as f:
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pickle.dump(bounds, f)
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logger.debug(f"Saved pivot bounds to cache for {symbol}")
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except Exception as e:
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logger.warning(f"Error saving pivot bounds to cache for {symbol}: {e}")
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def _load_monthly_data_from_cache(self, symbol: str) -> Optional[pd.DataFrame]:
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"""Load monthly 1m data from cache"""
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try:
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cache_file = self.monthly_data_cache_dir / f"{symbol.replace('/', '')}_monthly_1m.parquet"
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if cache_file.exists():
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try:
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df = pd.read_parquet(cache_file)
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# Ensure cached monthly data has proper timezone (UTC to match COB WebSocket data)
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if not df.empty and 'timestamp' in df.columns:
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if df['timestamp'].dt.tz is None:
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# If no timezone info, assume UTC and keep in UTC
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df['timestamp'] = pd.to_datetime(df['timestamp'], utc=True)
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elif str(df['timestamp'].dt.tz) != 'UTC':
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# Convert to UTC if different timezone
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df['timestamp'] = df['timestamp'].dt.tz_convert('UTC')
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logger.info(f"Loaded {len(df)} 1m candles from cache for {symbol}")
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return df
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except Exception as parquet_e:
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# Handle corrupted Parquet file - expanded error detection
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error_str = str(parquet_e).lower()
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corrupted_indicators = [
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"parquet magic bytes not found",
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"corrupted",
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"couldn't deserialize thrift",
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"don't know what type",
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"invalid parquet file",
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"unexpected end of file",
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"invalid metadata"
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]
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if any(indicator in error_str for indicator in corrupted_indicators):
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logger.warning(f"Corrupted Parquet cache file for {symbol}, removing and returning None: {parquet_e}")
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try:
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cache_file.unlink() # Delete corrupted file
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logger.info(f"Deleted corrupted monthly cache file: {cache_file}")
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except Exception as delete_e:
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logger.error(f"Failed to delete corrupted monthly cache file: {delete_e}")
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return None
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else:
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raise parquet_e
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logger.debug(f"Could not load pivot bounds from DuckDB for {symbol}: {e}")
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return None
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except Exception as e:
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logger.warning(f"Error loading monthly data from cache for {symbol}: {e}")
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return None
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def _save_monthly_data_to_cache(self, symbol: str, df: pd.DataFrame):
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"""Save monthly 1m data to cache"""
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try:
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cache_file = self.monthly_data_cache_dir / f"{symbol.replace('/', '')}_monthly_1m.parquet"
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df.to_parquet(cache_file, index=False)
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logger.info(f"Saved {len(df)} monthly 1m candles to cache for {symbol}")
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except Exception as e:
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logger.warning(f"Error saving monthly data to cache for {symbol}: {e}")
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def get_pivot_bounds(self, symbol: str) -> Optional[PivotBounds]:
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"""Get pivot bounds for a symbol"""
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return self.pivot_bounds.get(symbol)
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@@ -30,8 +30,8 @@ class DuckDBStorage:
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self.db_path = Path(db_path)
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self.db_path.parent.mkdir(parents=True, exist_ok=True)
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# Parquet storage directory
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self.parquet_dir = self.db_path.parent / "parquet_store"
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# Parquet storage directory (only for annotation snapshots)
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self.parquet_dir = self.db_path.parent / "annotation_snapshots"
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self.parquet_dir.mkdir(parents=True, exist_ok=True)
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# Connect to DuckDB
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@@ -41,12 +41,42 @@ class DuckDBStorage:
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self._init_schema()
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logger.info(f"DuckDB storage initialized: {self.db_path}")
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logger.info(f"Parquet storage: {self.parquet_dir}")
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logger.info(f"Annotation snapshots: {self.parquet_dir}")
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def _init_schema(self):
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"""Initialize database schema with Parquet integration"""
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"""Initialize database schema - all data in DuckDB tables"""
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# Create annotations table (metadata only)
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# Create OHLCV data table - stores ALL candles
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self.conn.execute("""
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CREATE SEQUENCE IF NOT EXISTS ohlcv_id_seq START 1
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""")
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self.conn.execute("""
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CREATE TABLE IF NOT EXISTS ohlcv_data (
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id INTEGER PRIMARY KEY DEFAULT nextval('ohlcv_id_seq'),
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symbol VARCHAR NOT NULL,
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timeframe VARCHAR NOT NULL,
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timestamp BIGINT NOT NULL,
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open DOUBLE NOT NULL,
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high DOUBLE NOT NULL,
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low DOUBLE NOT NULL,
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close DOUBLE NOT NULL,
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volume DOUBLE NOT NULL,
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created_at BIGINT NOT NULL,
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UNIQUE(symbol, timeframe, timestamp)
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)
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""")
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# Create indexes for fast queries
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self.conn.execute("""
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CREATE INDEX IF NOT EXISTS idx_ohlcv_symbol_timeframe
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ON ohlcv_data(symbol, timeframe)
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""")
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self.conn.execute("""
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CREATE INDEX IF NOT EXISTS idx_ohlcv_timestamp
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ON ohlcv_data(timestamp)
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""")
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||||
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# Create annotations table
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self.conn.execute("""
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CREATE TABLE IF NOT EXISTS annotations (
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annotation_id VARCHAR PRIMARY KEY,
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||||
@@ -72,7 +102,7 @@ class DuckDBStorage:
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||||
CREATE TABLE IF NOT EXISTS cache_metadata (
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symbol VARCHAR NOT NULL,
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timeframe VARCHAR NOT NULL,
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||||
parquet_path VARCHAR NOT NULL,
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||||
parquet_path VARCHAR,
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||||
first_timestamp BIGINT NOT NULL,
|
||||
last_timestamp BIGINT NOT NULL,
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||||
candle_count INTEGER NOT NULL,
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||||
@@ -81,11 +111,11 @@ class DuckDBStorage:
|
||||
)
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||||
""")
|
||||
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||||
logger.info("DuckDB schema initialized")
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||||
logger.info("DuckDB schema initialized (all data in tables)")
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||||
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||||
def store_ohlcv_data(self, symbol: str, timeframe: str, df: pd.DataFrame) -> int:
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"""
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Store OHLCV data as Parquet file and register in DuckDB
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Store OHLCV data directly in DuckDB table
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||||
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Args:
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||||
symbol: Trading symbol
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||||
@@ -113,38 +143,41 @@ class DuckDBStorage:
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||||
# Add metadata
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||||
df_copy['symbol'] = symbol
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||||
df_copy['timeframe'] = timeframe
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||||
df_copy['created_at'] = int(datetime.now().timestamp() * 1000)
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||||
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||||
# Define parquet file path
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parquet_file = self.parquet_dir / f"{symbol.replace('/', '_')}_{timeframe}.parquet"
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# Select columns in correct order
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||||
columns = ['symbol', 'timeframe', 'timestamp', 'open', 'high', 'low', 'close', 'volume', 'created_at']
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df_insert = df_copy[columns]
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||||
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||||
# Load existing data if file exists
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||||
if parquet_file.exists():
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||||
try:
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existing_df = pd.read_parquet(parquet_file)
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||||
# Combine with new data
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||||
df_copy = pd.concat([existing_df, df_copy], ignore_index=True)
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||||
# Remove duplicates
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||||
df_copy = df_copy.drop_duplicates(subset=['timestamp'], keep='last')
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||||
df_copy = df_copy.sort_values('timestamp')
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||||
except Exception as e:
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||||
logger.warning(f"Could not load existing parquet: {e}")
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||||
# Insert data directly into DuckDB (ignore duplicates)
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||||
# Note: id column is auto-generated, so we don't include it
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||||
self.conn.execute("""
|
||||
INSERT INTO ohlcv_data (symbol, timeframe, timestamp, open, high, low, close, volume, created_at)
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||||
SELECT symbol, timeframe, timestamp, open, high, low, close, volume, created_at
|
||||
FROM df_insert
|
||||
ON CONFLICT DO NOTHING
|
||||
""")
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||||
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||||
# Save to parquet
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||||
df_copy.to_parquet(parquet_file, index=False, compression='snappy')
|
||||
# Update metadata
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||||
result = self.conn.execute("""
|
||||
SELECT
|
||||
MIN(timestamp) as first_ts,
|
||||
MAX(timestamp) as last_ts,
|
||||
COUNT(*) as count
|
||||
FROM ohlcv_data
|
||||
WHERE symbol = ? AND timeframe = ?
|
||||
""", (symbol, timeframe)).fetchone()
|
||||
|
||||
# Update metadata in DuckDB
|
||||
first_ts = int(df_copy['timestamp'].min())
|
||||
last_ts = int(df_copy['timestamp'].max())
|
||||
count = len(df_copy)
|
||||
first_ts, last_ts, count = result
|
||||
now_ts = int(datetime.now().timestamp() * 1000)
|
||||
|
||||
self.conn.execute("""
|
||||
INSERT OR REPLACE INTO cache_metadata
|
||||
(symbol, timeframe, parquet_path, first_timestamp, last_timestamp, candle_count, last_update)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?)
|
||||
""", (symbol, timeframe, str(parquet_file), first_ts, last_ts, count, now_ts))
|
||||
""", (symbol, timeframe, '', first_ts, last_ts, count, now_ts))
|
||||
|
||||
logger.info(f"Stored {len(df)} candles for {symbol} {timeframe} in Parquet (total: {count})")
|
||||
logger.info(f"Stored {len(df)} candles for {symbol} {timeframe} in DuckDB (total: {count})")
|
||||
return len(df)
|
||||
|
||||
except Exception as e:
|
||||
@@ -158,7 +191,7 @@ class DuckDBStorage:
|
||||
end_time: Optional[datetime] = None,
|
||||
limit: Optional[int] = None) -> Optional[pd.DataFrame]:
|
||||
"""
|
||||
Query OHLCV data directly from Parquet using DuckDB
|
||||
Query OHLCV data directly from DuckDB table
|
||||
|
||||
Args:
|
||||
symbol: Trading symbol
|
||||
@@ -171,26 +204,10 @@ class DuckDBStorage:
|
||||
DataFrame with OHLCV data
|
||||
"""
|
||||
try:
|
||||
# Get parquet file path from metadata
|
||||
result = self.conn.execute("""
|
||||
SELECT parquet_path FROM cache_metadata
|
||||
WHERE symbol = ? AND timeframe = ?
|
||||
""", (symbol, timeframe)).fetchone()
|
||||
|
||||
if not result:
|
||||
logger.debug(f"No data found for {symbol} {timeframe}")
|
||||
return None
|
||||
|
||||
parquet_path = result[0]
|
||||
|
||||
if not Path(parquet_path).exists():
|
||||
logger.warning(f"Parquet file not found: {parquet_path}")
|
||||
return None
|
||||
|
||||
# Build query - DuckDB can query Parquet directly!
|
||||
query = f"""
|
||||
# Build query
|
||||
query = """
|
||||
SELECT timestamp, open, high, low, close, volume
|
||||
FROM read_parquet('{parquet_path}')
|
||||
FROM ohlcv_data
|
||||
WHERE symbol = ? AND timeframe = ?
|
||||
"""
|
||||
params = [symbol, timeframe]
|
||||
@@ -219,7 +236,7 @@ class DuckDBStorage:
|
||||
df = df.set_index('timestamp')
|
||||
df = df.sort_index()
|
||||
|
||||
logger.debug(f"Retrieved {len(df)} candles for {symbol} {timeframe} from Parquet")
|
||||
logger.debug(f"Retrieved {len(df)} candles for {symbol} {timeframe} from DuckDB")
|
||||
return df
|
||||
|
||||
except Exception as e:
|
||||
|
||||
Reference in New Issue
Block a user